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1The Predictive Privacy Project - Rainer Mühlhoff
Data Protection in the Context of Big Data and AI
2 years ago by @hotho
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privacy
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machine
Datenschutz
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ml
deepprivacydatamachineDatenschutzpredictivelearningbigml
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1NeurIPS 2022 Gaze Meets ML Workshop
https://groups.google.com/g/ml-news/c/F0JgTZQL-Ns/m/H95PMQyTAgAJ?pli=1
2 years ago by @becker
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gaze
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tracking
eyegazevisualfocusmachineailearningimmermindtracking
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1Healthcare Algorithms Don’t Always Need to Be Generalizable
A Stanford researcher questions the need for generalizable models and proposes instead sharing recipes for creating useful local models.
2 years ago by @becker
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hospital
generalizationmachineailearningmodelgeneralizehealthhospital
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1GitHub - grok-ai/nn-template: Generic template to bootstrap your PyTorch project.
https://github.com/grok-ai/nn-template
2 years ago by @becker
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codecookieneuralconfigurationmachinecutterlearningnetworkconfigprogramming
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1How to be a machine learning biologist
https://twitter.com/natstann/status/1516428383432941571?t=RGNmFPvykzWjdvYCkoN2oA&s=19
2 years ago by @becker
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1Predicting algorithms - machine learning for index structures
https://www.quantamagazine.org/machine-learning-reimagines-the-building-blocks-of-computing-20220315/
2 years ago by @becker
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1Machine Learning: TensorFlow-Benchmarks auf MacBooks mit M1 Pro und M1 Max | heise online
https://www.heise.de/tests/Machine-Learning-TensorFlow-Benchmarks-auf-MacBooks-mit-M1-Pro-und-M1-Max-6328476.html?seite=all
2 years ago by @hotho
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1microsoft/responsible-ai-toolbox: This project provides responsible AI user interfaces for Fairlearn, interpret-community, and Error Analysis, as well as foundational building blocks that they rely on.
https://github.com/microsoft/responsible-ai-toolbox
2 years ago by @becker
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1Deterministic python
https://twitter.com/kastnerkyle/status/1473361479143460872?t=-q6YbOcOZ2AXJ_EgW0vkJg&s=19
2 years ago by @becker
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torch
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1‪Sara Hajian‬ - ‪Google Scholar‬
https://scholar.google.de/citations?hl=en&user=rXY4178AAAAJ&view_op=list_works&sortby=pubdate
3 years ago by @becker
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fairness
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1LOGML - Marinka Zitnik: Graph Representation Learning for Biomedical Discovery - YouTube
https://www.youtube.com/watch?v=hOfcOak-moU&t=1s
3 years ago by @becker
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2CORNELL CS4780 "Machine Learning for Intelligent Systems" - YouTube
https://www.youtube.com/playlist?list=PLl8OlHZGYOQ7bkVbuRthEsaLr7bONzbXS
3 years ago by @becker
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kilian
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119: Boosting
https://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/lecturenote19.html
3 years ago by @becker
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kilian
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4How to pick more beautiful colors for your data visualizations | Chartable
https://blog.datawrapper.de/beautifulcolors/
4 years ago by @becker
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visualization
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choose
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pick
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picker
chose
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2Transfer learning from pre-trained models | by Pedro Marcelino | Towards Data Science
https://towardsdatascience.com/transfer-learning-from-pre-trained-models-f2393f124751
4 years ago by @becker
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transfer
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size
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learning
to
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nn
transferdatasetneuralsizeusemachinelearningtonetworkwhenmlnn
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1GFLASSO: Graph-Guided Fused LASSO in R - DataCamp
https://www.datacamp.com/community/tutorials/gflasso-R
4 years ago by @becker
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multilabel
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guided
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multi
multilabelrguidedlassomachinelearninglabelgraphmulti
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1mims-harvard/scikit-fusion: scikit-fusion: Data fusion via collective latent factor models
https://github.com/mims-harvard/scikit-fusion
4 years ago by @becker
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multiview
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py
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gpu
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skfusion
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factor
multiviewpythondatapylearninggpuheterogeneousnetworkskfusionmatrixfactorizationgraphsklearngraphslatentmitfusionmachinescikitfactor
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2Statistical Significance Tests for Comparing Machine Learning Algorithms
https://machinelearningmastery.com/statistical-significance-tests-for-comparing-machine-learning-algorithms/
4 years ago by @becker
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comparison
statistical
test
data
learning
science
significance
mining
cross
machine
testing
hypothesis
validation
comparisonstatisticaltestdatalearningsciencesignificanceminingcrossmachinetestinghypothesisvalidation
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4Interpretable Machine Learning
https://christophm.github.io/interpretable-ml-book/
4 years ago by @becker
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artifical
machine
ai
learning
interpretable
explainable
xai
ml
intelligence
artificalmachineailearninginterpretableexplainablexaimlintelligence
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1How to use Data Scaling Improve Deep Learning Model Stability and Performance
https://machinelearningmastery.com/how-to-improve-neural-network-stability-and-modeling-performance-with-data-scaling/
4 years ago by @becker
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output
input
scaling
deep
data
machine
butterfly
project
learning
outputinputscalingdeepdatamachinebutterflyprojectlearning
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publications (hide)93
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3Marina: Realizing ML-Driven Real-Time Network Traffic Monitoring at Terabit Scale
M. Seufert, K. Dietz, N. Wehner, S. Geißler, J. Schüler, M. Wolz, A. Hotho, P. Casas, T. Hoßfeld, and A. Feldmann. IEEE Transactions on Network and Service Management, (2024)
24 days ago by @hotho
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myown
monitoring
machine
2024
learning
network
traffic
myownmonitoringmachine2024learningnetworktraffic
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1Computational Approaches for Connecting Maternal Stress to Preterm Birth
A. Mirzaei, B. Hiller, I. Stelzer, K. Thiele, Y. Tan, and M. Becker. Clinics in Perinatology, (2024)
a month ago by @becker
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myown
stress
pregnancy
machine
learning
computational
myownstresspregnancymachinelearningcomputational
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1Feature-weighted elastic net: using "features of features" for better prediction
J. Tay, N. Aghaeepour, T. Hastie, and R. Tibshirani. Statistica Sinica, 33 (1): 259--279 (January 2023)
3 months ago by @becker
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ien
linear
background
machine
regression
learning
knowledge
bki
ml
informed
integration
ienlinearbackgroundmachineregressionlearningknowledgebkimlinformedintegration
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2Utilizing Molecular Network Information via Graph Convolutional Neural Networks to Predict Metastatic Event in Breast Cancer
H. Chereda, A. Bleckmann, F. Kramer, A. Leha, and T. Beissbarth. Studies in health technology and informatics, (September 2019)
3 months ago by @becker
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neural
background
gnn
machine
learning
knowledge
networks
graph
bki
ml
informed
integration
neuralbackgroundgnnmachinelearningknowledgenetworksgraphbkimlinformedintegration
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1Whole genome deconvolution unveils Alzheimer’s resilient epigenetic signature
E. Berson, A. Sreenivas, T. Phongpreecha, A. Perna, F. Grandi, L. Xue, N. Ravindra, N. Payrovnaziri, S. Mataraso, Y. Kim and 9 other author(s). Nature Communications, 14 (1): 4947 (2023)
7 months ago by @becker
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deconvolution
genome
alzheimer
myown
epigenetics
machine
learning
deconvolutiongenomealzheimermyownepigeneticsmachinelearning
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1Hidden stratification causes clinically meaningful failures in machine learning for medical imaging
L. Oakden-Rayner, J. Dunnmon, G. Carneiro, and C. Ré. Proceedings of the ACM conference on health, inference, and learning, page 151--159. (2020)
11 months ago by @becker
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subgroup
mining
failure
stratification
hidden
machine
pattern
learning
citedby:scholar:count:243
citedby:scholar:timestamp:2023-5-31
subgroupminingfailurestratificationhiddenmachinepatternlearningcitedby:scholar:count:243citedby:scholar:timestamp:2023-5-31
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2Machine learning techniques for accountability
B. Kim, and F. Doshi-Velez. AI Magazine, 42 (1): 47--52 (2021)
a year ago by @becker
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artificial
responsible
machine
ai
citedby:scholar:count:15
learning
intelligence
ml
citedby:scholar:timestamp:2023-4-10
accountability
artificialresponsiblemachineaicitedby:scholar:count:15learningintelligencemlcitedby:scholar:timestamp:2023-4-10accountability
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1Revealing the impact of lifestyle stressors on the risk of adverse pregnancy outcomes with multitask machine learning
M. Becker, J. Dai, A. Chang, D. Feyaerts, I. Stelzer, M. Zhang, E. Berson, G. Saarunya, D. De Francesco, C. Espinosa and 1 other author(s). Frontiers in Pediatrics, (2022)
a year ago by @becker
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multitask
myown
immune
stress
pregnancy
learning
life
stressors
cell
system
outcomes
machine
adverse
immunology
single
multitaskmyownimmunestresspregnancylearninglifestressorscellsystemoutcomesmachineadverseimmunologysingle
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1Machine Learning Techniques
J. Choi, N. Aghaeepour, and M. Becker. page 147--164. Springer International Publishing, Cham, (2022)
a year ago by @becker
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chapter
myown
book
machine
learning
surgery
chaptermyownbookmachinelearningsurgery
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4Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning
P. Villalobos, J. Sevilla, L. Heim, T. Besiroglu, M. Hobbhahn, and A. Ho. arXiv preprint arXiv:2211.04325, (2022)
a year ago by @becker
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set
models
deep
large
sets
data
learning
requirement
datasets
dataset
machine
limit
model
themis
setmodelsdeeplargesetsdatalearningrequirementdatasetsdatasetmachinelimitmodelthemis
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2The Deskilling of Domain Expertise in AI Development
N. Sambasivan, and R. Veeraraghavan. CHI Conference on Human Factors in Computing Systems, page 1--14. (2022)
a year ago by @becker
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domain
artificial
deskilling
machine
citedby:scholar:count:13
ai
learning
knowledge
expertise
themis
intelligence
citedby:scholar:timestamp:2022-12-8
domainartificialdeskillingmachinecitedby:scholar:count:13ailearningknowledgeexpertisethemisintelligencecitedby:scholar:timestamp:2022-12-8
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2Why do tree-based models still outperform deep learning on typical tabular data?
L. Grinsztajn, E. Oyallon, and G. Varoquaux. Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track, (2022)
a year ago by @becker
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comparison
forest
decision
neural
data
tabular
learning
tree
trees
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performance
random
citedby:scholar:count:5
evaluation
citedby:scholar:timestamp:2022-12-1
machine
ml
nn
comparisonforestdecisionneuraldatatabularlearningtreetreesnetworkperformancerandomcitedby:scholar:count:5evaluationcitedby:scholar:timestamp:2022-12-1machinemlnn
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1Interpretable machine learning prediction of all-cause mortality
W. Qiu, H. Chen, A. Dincer, S. Lundberg, M. Kaeberlein, and S. Lee. medRxiv, (2022)
a year ago by @becker
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supervised
distance
citedby:scholar:count:3
machine
collaboration
fuelln
learning
interpretable
citedby:scholar:timestamp:2022-11-30
ml
superviseddistancecitedby:scholar:count:3machinecollaborationfuellnlearninginterpretablecitedby:scholar:timestamp:2022-11-30ml
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3Algorithmic fairness datasets: the story so far
A. Fabris, S. Messina, G. Silvello, and G. Susto. Data Mining and Knowledge Discovery, 36 (6): 2074--2152 (Nov 1, 2022)
a year ago by @becker
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fairness
toread
machine
learning
algorithmic
themis
fairnesstoreadmachinelearningalgorithmicthemis
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1A guide to machine learning for biologists
J. Greener, S. Kandathil, L. Moffat, and D. Jones. Nature Reviews Molecular Cell Biology, 23 (1): 40--55 (2022)
2 years ago by @becker
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nature
machine
learning
guide
citedby:scholar:timestamp:2022-6-29
biologists
citedby:scholar:count:72
naturemachinelearningguidecitedby:scholar:timestamp:2022-6-29biologistscitedby:scholar:count:72
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1Explaining deep neural networks and beyond: A review of methods and applications
W. Samek, G. Montavon, S. Lapuschkin, C. Anders, and K. Müller. Proceedings of the IEEE, 109 (3): 247--278 (2021)
2 years ago by @becker
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neural
citedby:scholar:count:193
machine
citedby:scholar:timestamp:2022-6-27
learning
explainable
networks
themis
neuralcitedby:scholar:count:193machinecitedby:scholar:timestamp:2022-6-27learningexplainablenetworksthemis
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1A biomedical open knowledge network harnesses the power of AI to understand deep human biology
S. Baranzini, K. Börner, J. Morris, C. Nelson, K. Soman, E. Schleimer, M. Keiser, M. Musen, R. Pearce, T. Reza and 1 other author(s). Ai Magazine, 43 (1): 46--58 (2022)
2 years ago by @becker
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biology
citedby:scholar:timestamp:2022-6-21
ai
learning
biomedical
network
graph
citedby:scholar:count:1
background
machine
human
knowledge
open
biologycitedby:scholar:timestamp:2022-6-21ailearningbiomedicalnetworkgraphcitedby:scholar:count:1backgroundmachinehumanknowledgeopen
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2Integrating Machine Learning with Human Knowledge
C. Deng, X. Ji, C. Rainey, J. Zhang, and W. Lu. iScience, 23 (11): 101656 (2020)
2 years ago by @becker
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background
machine
review
learning
survey
knowledge
integration
backgroundmachinereviewlearningsurveyknowledgeintegration
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1Leukocyte telomere dynamics across gestation in uncomplicated pregnancies and associations with stress
D. Panelli, S. Leonard, R. Wong, M. Becker, J. Mayo, E. Wu, A. Girsen, I. Gotlib, N. Aghaeepour, M. Druzin and 1 other author(s). BMC Pregnancy and Childbirth, 22 (1): 1--12 (2022)
2 years ago by @becker
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telomeres
myown
stress
pregnancy
telomere
machine
learning
telomeresmyownstresspregnancytelomeremachinelearning
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5Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence
S. Raschka, J. Patterson, and C. Nolet. Information, 11 (4): 193 (2020)
2 years ago by @becker
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python
rapids
artifical
data
ai
py
learning
gpu
intelligence
paper:fastcor
science
library
machine
pythonrapidsartificaldataaipylearninggpuintelligencepaper:fastcorsciencelibrarymachine
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